An intelligent system for failure detection and control in an autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) have been used extensively in deep sea research. Failure detection and control is an important issue in maintaining the stability of an AUV. In most AUVs, the vehicle resurfaces in the event of minor failures such as in the depth sensor, the inclinometer, etc. The paper proposes an intelligent system for failure detection and control in AUVs where the vehicle could continue exploration in case of minor failures in the sensors and control surfaces. The intelligent system, based on the model proposed in Patel and Ranganathan (1996), integrates the adaptability of an artificial neural network (ANN) and the inferencing ability of a fuzzy rule based expert system on a single VLSI chip. The associative function of the ANN is used to recognize and detect the failures by observing the various changing parameters of the dynamic vehicle. The inferencing ability of an expert system suggests ways to control the failure and indicates the subsequent status of the vehicle. The entire system could be used as a low level diagnoser in an overall control system for AUVs.